README.md

---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_keras_callback
model-index:
- name: hausaBERTa
  results: []
datasets:
- mangaphd/hausaBERTdatatrain
language:
- ha
- af
---

<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->

# hausaBERTa

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) trained on mangaphd/hausaBERTdatatrain dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0151
- Train Accuracy: 0.9849
- Epoch: 2

The sentiment fine-tuning was done on Hausa Language.

Model Repository :  https://github.com/idimohammed/HausaBERTa

## Model description

HausaSentiLex is a pretrained lexicon low resources language model. The model was trained on Hausa Language (Hausa is a Chadic language spoken by the Hausa people in the northern half of Nigeria, Niger, Ghana, Cameroon, Benin and Togo, and the southern half of Niger, Chad and Sudan, with significant minorities in Ivory Coast. It is the most widely spoken language in West Africa, and one of the most widely spoken languages in Africa as a whole).
The model has been shown to obtain competitive downstream performances on text classification on trained language

## Intended uses & limitations

You can use this model with Transformers for sentiment analysis task in Hausa Language.

# Supplementary function
Add the following codes for ease of interpretation

import pandas as pd
def sentiment_analysis(text):
  rs = pipe(text)
  df = pd.DataFrame(rs)
  senti=df['label'][0]
  score=df['score'][0]
  if senti == 'LABEL_0' and score > 0.5:
    lb='NEGATIVE'
  elif senti == 'LABEL_1' and score > 0.5:
    lb='POSITIVE'
  else:
    lb='NEUTRAL'
  return lb

call sentiment_analysis('Your text here') while using the model

### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 5e-06, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Train Accuracy | Epoch |
|:----------:|:--------------:|:-----:|
| 0.2108     | 0.9168         | 0     |
| 0.1593     | 0.9385         | 1     |
| 0.0151     | 0.9849         | 2     |


### Framework versions

- Transformers 4.33.2
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.13.3